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Tracking the Digital Footprints of Personality


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A growing portion of offline and online human activities leave digital footprints in electronic databases. Resulting big social data offers unprecedented insights into population-wide patterns and detailed characteristics of the individuals. The goal of this paper is to review the literature showing how pervasive records of digital footprints, such as Facebook profile, or mobile device logs, can be used to infer personality, a major psychological framework describing differences in individual behavior. We briefly introduce personality and present a range of works focusing on predicting it from digital footprints and conclude with a discussion of the implications of these results in terms of privacy, data ownership, and opportunities for future research in computational social science.
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Tracking the Digital Footprints
of Personality
This paper reviews literature showing how pervasive records of digital footprints can be
used to infer personality.
By Renaud Lambiotte and Michal Kosinski
ABSTRACT |A growing portion of offline and online human
activities leave digital footprints in electronic databases.
Resulting big social data offers unprecedented insights into
population-wide patterns and detailed characteristics of the
individuals. The goal of this paper is to review the literature
showing how pervasive records of digital footprints, such as
Facebook profile, or mobile device logs, can be used to infer
personality, a major psychological framework describing
differences in individual behavior. We briefly introduce
personality and present a range of works focusing on
predicting it from digital footprints and conclude with a
discussion of the implications of these results in terms of
privacy, data ownership, and opportunities for future research
in computational social science.
KEYWORDS |Big data; personality; psychology; social networks
In recent years, a growing portion of human activities such
as social interactions and entertainment have become
mediated by digital services and devices. The records of
those activities, or ‘‘big social data,’’ are changing the
paradigm in the social sciences, as it undergoes a transition
from small-scale studies, typically employing question-
naires or lab-based observations and experiments, to large-
scale studies, in which researchers observe the behavior of
thousands or millions of individuals and search for
statistical regularities and underlying principles [1]–[6].
These works provide empirical observations at an unprec-
edented scale offering the potential to radically improve
our understanding of the individuals and social systems.
One of the major insights offered by big social data
research relates to the predictability of individuals’
psychological traits from their digital footprint [3]. Ability
to automatically assess psychological profiles opens the
way for improved products and services as personalized
search engines, recommender systems [7], and targeted
online marketing [8]. On the other hand, however, it
creates significant challenges in the areas of privacy [9],
[10]. The main goal of this paper is to provide a review of
the works investigating the potential of the big social data
to predict a five-factor model of personalityVthe major
set of psychological traitsVsupporting further studies of
the relationship between personality and digital footprint
and its implications for privacy and new products and
The most widespread and generally accepted model of
personality is the five-factor model of personality (FFM;
[11]). FFM was shown to subsume most known personality
traits, and it is claimed to represent the basic structure
underlying the variations in human behavior and prefer-
ences, providing a nomenclature and a conceptual
framework that unifies much of the research findings in
the psychology of individual differences. FFM includes the
following traits.
1) Openness is related to imagination, creativity,
curiosity, tolerance, political liberalism, and
appreciation for culture. People scoring high on
openness like change, appreciate new and unusual
ideas, and have a good sense of aesthetics.
Manuscript received January 29, 2014; revised July 24, 2014; accepted September 9,
2014. Date of publication October 29, 2014; date of current version November 18, 2014.
The work of R. Lambiotte was supported by the F.R.S.–Fonds de la Recherche
Scientifique (FNRS), the European Union (EU) project Optimizr, and COST Action TD1210
KnowEscape. The work of M. Kosinski was supported by the Psychometrics Centre at
the University of Cambridge, Boeing Corporation, Microsoft Research, the National
Science Foundation (NSF), the Defense Advanced Research Projects Agency (DARPA),
and Center for the Study of Language and Information at Stanford University (CLSI).
This paper presents results of the Belgian Network Dynamical Systems, Control, and
Optimization (DYSCO), funded by the Interuniversity Attraction Poles Programme,
initiated by the Belgian State, Science Policy Office.
R. Lambiotte is with the Namur Center for Complex Systems (naXys), University of
Namur, Namur 5000, Belgium (e-mail:
M. Kosinski is with InfoLab, Stanford University, Stanford, CA 94305 USA, and also
with the Psychometrics Centre, University of Cambridge, Cambridge CB2 1TN, U.K.
Digital Object Identifier: 10.1109/JPROC.2014.2359054
0018-9219 Ó2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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1934 Proceedings of the IEEE |Vol.102,No.12,December2014
2) Conscientiousness measures the preference for an
organized approach to life in contrast to a
spontaneous one. Conscientious people are more
likely to be well organized, reliable, and consis-
tent. They enjoy planning, seek achievements, and
pursue long-term goals. Nonconscientious indivi-
duals are generally more easygoing, spontaneous,
and creative. They tend to be more tolerant and
less bound by rules and plans.
3) Extroversion measures a tendency to seek stimu-
lation in the external world, the company of
others, and to express positive emotions. Extro-
verts tend to be more outgoing, friendly, and
socially active. They are usually energetic and
talkative; they do not mind being at the center of
attention and make new friends more easily.
reserved and seek environments characterized by
lower levels of external stimulation.
4) Agreeableness relates to a focus on maintaining
positive social relations, being friendly, compas-
sionate, and cooperative. Agreeable people tend to
trust others and adapt to their needs. Disagreeable
people are more focused on themselves, less likely
to compromise, and may be less gullible. They also
tend to be less bound by social expectations and
conventions and are more assertive.
5) Emotional stability (opposite referred to as
neuroticism) measures the tendency to experi-
ence mood swings and emotions, such as guilt,
anger, anxiety, and depression. Emotionally un-
stable (neurotic) people are more likely to
experience stress and nervousness, whereas emo-
tionally stable people (low neuroticism) tend to be
calmer and self-confident.
Research has shown that personality is correlated with
many aspects of life, including job success [12], attractive-
ness [13], drug use [14], marital satisfaction [15], infidelity
[16], and happiness [17]. The main limitations of classical
personality studies are, however, the size of the samples,
often too poor for statistical validation, and their strong
bias toward white, educated, industrialized, rich, and
democratic (WEIRD) people [18].
The increasingly prevalent access to digital media enables
large-scale online projects aimed at collecting personality
profiles and exploring their relations with digital foot-
prints. Personality has been investigated through different
types of online media, for instance, by focusing on website
browsing logs [2], [19], contents of personal websites [20],
music collections [21], or properties of Twitter profiles
[22], [23].
The most complete online social environment is
arguably Facebook, due to its popularity and rich social
and semantic data stored on its users’ profiles that can be
conveniently recorded. It is important to note that
Facebook profiles are increasingly becoming a channel
through which to form impressions about others, for
example, before dating [24] or before a job interview [25].
Moreover, research tends to show that a Facebook profile
reflects the actual personality of an individual rather than
an idealized role [26], and that personality can be
successfully judged by the others based on Facebook
profiles [27], [28]. These results suggest that personality is
manifested not only in the offline, but also online
behavior, and thus digital footprints can be used to
predict it.
The most popular data set used to study the
relationship between personality and digital footprint
comes from the myPersonality project. myPersonality was
a Facebook application set up by David Stillwell in 2007
that offered participants access to 25 psychological tests
and attracted over six million users. myPersonality users
received immediate feedback (see Fig. 1) on their results
and could donate their Facebook profile information to
research resulting in a database that, after anonymization,
is being shared with the academic community at, allowing for the study of hitherto
unanswered questions in a wide range of topics, such as
geographical variations in personality ([29]; see Fig. 2),
social networks [2], [22], [30], [31], privacy [32], language
[6] (see Fig. 3), predicting individual traits [33], [3],
computer science [34], happiness [35], music [36], and
delayed discounting [37].
Social network structure is one of the major types of digital
footprint left by the users, and a growing number of studies
shows that it is predictive of often intimate personal traits.
For instance, it is known that the location within a
Facebook friendship network is predictive of sexual
orientation [38]. Similarly, it is possible to accurately
detect users’ romantic partner by observing overlap in
social circles [39].
Fig. 1. Snapshot of a personality profile generated by the
myPersonality Facebook App, representing an individual thatis liberal
and open minded (high openness), well-organized (high
conscientiousness), contemplative and happy with own company (low
extroversion), of average competitiveness (average
agreeableness), and laid back and relaxed (low neuroticism).
Lambiotte and Kosinski: Tracking the Digital Footprints of Personality
Vol. 102, No. 12, December 2014 | Proceedings of the IEEE 1935
Personality is expected to affect people’s social
of social ties formed by people. There are a number of
studies exploring this relationship. Neuroticism is usually
associated with negative social interactions, while extro-
version positively correlates with the size of the network
and greater social status [40], [41]. Results related to the
remaining traits tend to be inconsistent, perhaps due to
small sample sizes. More recently, Quercia et al. [31] used
myPersonality data set to study the relation between
sociometric popularity and personality traits, at a scale
several orders of magnitudes larger than in the previous
studies. They have shown that the strongest predictor for
the number of friends is extroversion, while other
personality traits do not play a significant role. On
average, extreme extroverts tend to have twice as many
friends as extreme introverts. A subsequent work [42]
went one step further and, for the first time, quantitatively
explained the way in which egocentric network topology is
shaped by personality. It confirmed that extroversion plays
a major role by showing that introverts are part of fewer
but larger communities, whereas extroverts tend to act as
bridges between more frequent but smaller communities
(see Fig. 4).
The Facebook profile of a user is not purely demographic,
as it also contains robust records of digital footprints. In
particular, Facebook likes exemplify a typical variety of
digital footprintVa connection between the user and a
content that is similar to other pervasive records such as
playlists (see Fig. 5), website browsing logs, purchase
records, or web search queries. A recent paper [3] based
on the myPersonality database and using relatively
straightforward methods (singular value decomposition
and linear regression) showed that Facebook likes are
highly predictive of personality and number of other
psychodemographic traits, such as age, gender, intelli-
gence, political and religious views, and sexual orientation
(see Fig. 6). The paper provided examples of likes most
strongly associated with given personality traits. For
Fig. 3. Words, phrases, and topics most distinguishing extroversion
from introversion. Source: [6].
Fig. 2. Personality maps of U.S. states for neuroticism (upper) and
extroversion (lower). Dark (light) blue indicates values higher (lower)
than average. Figure based on myPersonality data.
Fig. 4. Typical egocentric networks of introverts (left) and extroverts
(right). Introverts tend to belong to fewer but larger and denser
communities, while extroverts tend to act as bridges between more
frequent,smaller, andoverlappingcommunities.Connections between
Ego and his friends have not been depicted for the sake of clarity.
Lambiotte and Kosinski: Tracking the Digital Footprints of Personality
1936 Proceedings of the IEEE |Vol.102,No.12,December2014
have high openness, low conscientiousness, and low
Similar predictions can be based on the textual analysis of
people’s posts and other samples of text. There is a long
tradition in using text to infer personality [44], [45], [46],
however, never at the scale presented in [6]. This study
applied differential language analysis to uncover features
distinguishing demographic and psychological attributes to
700 million words, phrases, and topic instances collected
by myPersonality from Facebook status updates of 75 000
participants. It showed a striking variations of language
driven by personality, gender, and age. This work has not
only confirmed existing observations (such as neurotic
people’s tendency to use the word ‘‘depressed’’), but also
posed new hypotheses (such as a relationship between
physical activity and low neuroticism).
The proliferation of mobile-devices loaded with sensors
means that offline human activities are also increasingly
leaving digital footprint [47], [48]. For instance, physical
states such as running or walking can be inferred from
accelerometer data; colocation with other devices can be
detected using Bluetooth; geolocation can be established
using WiFi, Global Positioning System (GPS), or Global
System for Mobile (GSM) triangulation; and social
interactions can be measured by records of text messages
and phone calls. These data can be recorded by dedicated
apps, such as EmotionSense [49], which measures
emotional states based on the speech patterns and matches
it with physical activity, geolocation, and colocation with
other users. In the last few years, call data records (CDRs)
have been used to study the organization of social networks
and human mobility [50], [51], [52].
Similarly to digital footprints left in the online
environment, offline activities recorded with mobile
devices’ sensors reflect users’ personality. A recent study
combined CDRs with personality profiles of mobile device
users and identified a number of mobility and social factors
correlated with personality [53]. For instance, mobility
indicators, such as distance traveled, significantly correlate
with neuroticism, while social life indicators, such as the
size of the social network, correlated with extroversion, in
agreement with the previous results based on online digital
Fig. 6. Prediction accuracy of regression for numeric attributes and
traits expressed by the Pearson correlation coefficient between
predicted and actual attribute values; all correlations are significant at
the pG0:001 level. The red outline bars indicate the questionnaire’s
baseline accuracy, expressed in terms of test-retest reliability.
Source: [3].
Fig. 5. Dendrogram illustrating the structure of music tastes and its
relation ship to the persona lity trait of ope nness among myPe rsonality
users. The structure was produced using hierarchical clustering of
the most popular Facebook likes from musician/band category. The
color scalerepresents the averageopenness of its subscribers, ranging
from conservative (cyan) to liberal (magenta). The height of the
nodes is proportional to the dissimilarity between individual likes or
clustersat both ends. The shorteris the path between two musicians or
bands, the larger overlap in audience. Source: [43].
Lambiotte and Kosinski: Tracking the Digital Footprints of Personality
Vol. 102, No. 12, December 2014 | Proceedings of the IEEE 1937
The main purpose of this paper was to review the evidence
of the relationship between digital footprint and person-
ality. We have shown that a wide range of pervasive and
often publicly available digital footprints such as Facebook
profiles or data from mobile devices can be used to infer
personality. As our life is increasingly interwoven with
digital services and devices, it is becoming critical to
understand the consequences of the apparent ability to
automatically and rapidly assess people’s psychological
Works cited in this paper indicate that the accuracy of
the personality predictions is moderate, with typical
correlation between the prediction and personality in the
range of r¼0:2andr¼0:4. It has to be noted, however,
that the ground truth (i.e., personality scores) is also
merely an approximation of the underlying latent traits. For
example, the accuracy of the personality scales used in [3]
expressed as a correlation between scores achieved by the
same person in two points of time (test-retest reliability)
ranged between r¼0:55 and r¼0:75. It is reasonable to
expect that with, an increasing amount of data available and
improved methods, assessment accuracy will improve.
Predicting users’ personality can be used to improve
numerous products and services. Digital systems and
devices (such as online stores or cars) could be designed to
adjust their behavior to best fit their users’ inferred profiles
[54]. For example, a car could adjust the parameters of the
engine and the music to the personality and current mood
of the driver. Also, the relevance of marketing and product
recommendations could be improved by adding psycho-
logical dimensions to current user models. For example,
online insurance advertisements might emphasize security
when facing emotionally unstable (neurotic) users but
stress potential threats when dealing with emotionally
stable ones. Moreover, digital footprint may provide a
convenient and reliable way to measure psychological
traits at a low cost. Such automated assessment could
prove to be more accurate and less prone to cheating and
misrepresentation than traditional questionnaires.
Furthermore, it is likely that new insights into
individual differences in human behavior offered by big
social data will fuel the emergence of new, more accurate,
robust models describing individuals and societies [5]. The
translation of big social data into models and policies calls
for a new wave of multidisciplinary collaborations between
fields as diverse as psychology, social sciences, linguistics,
computer science, and applied mathematics (perhaps
under the banner of computational social psychology).
On the other hand, the results presented here may
have considerable negative implications because it can
easily be applied to large numbers of people without
obtaining their individual consent and without them
noticing. Commercial companies, governmental institu-
tions, or even one’s Facebook friends could use software
to infer personality (and other attributes, such as
intelligence or sexual orientation) that an individual
may not have intended to share. There is a risk that the
growing awareness of such digital exposure may decrease
their trust in digital technologies, or even completely
deter them from them. We hope that researchers, policy
makers, and customers will find solutions to address those
challenges and retain the balance between the promises
and perils of the Digital Age. h
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Renaud Lambiotte received the Ph.D. degree in
theoretical physics from the Universite
´Libre de
Bruxelles, Brussels, Belgium, in 2004.
He is a Professor in the Department of Mathe-
was a Research Associate at the E
´cole normale
´rieure de Lyon (ENS Lyon), Lyon, France;
´de Lie
´ge, Lie
´ge, Belgium; Universite
catholique de Louvain, Louvain-la-Neuve, Belgium;
and Imperial College London, London, U.K. His
research interests include network science, data mining, stochastic
processes, social dynamics, and neuroimaging.
Michal Kosinski received the Ph.D. degree in
psychology and computer science from the Uni-
versity of Cambridge, Cambridge, U.K., in 2014.
He is a Research Associate at the Computer
Science Department, Stanford University, Stanford,
CA, USA and the Deputy Director of the Psycho-
metrics Centre, University of Cambridge. He studies
big social data and its consequences for privacy,
occupational markets, and wellbeing. He also
coordinates the myPersonality project, which in-
volves global collaboration between over 150 researchers analyzing a
sample of over eight million Facebook users.
Lambiotte and Kosinski: Tracking the Digital Footprints of Personality
Vol. 102, No. 12, December 2014 | Proceedings of the IEEE 1939
... Research in personality prediction has gone through remarkable developments in the recent years with results suggesting the possibility to predict individual personality trait levels from a range of digital behaviors [15,19,20]. The recent years have also shown that personality theory and prediction largely happen in two different disciplines -personality psychology and computer science. ...
... Online actions and motivations take place and are perceived as voluntary, even resulting from polls and calculations (Lambiotte & Kosinski, 2014), in which the mechanism to produce massification is displaced and decentralized, thus the center is not occupied by its mode of operation, though the "subject" seems to be autonomous. This way, together with the permission of anyone to instrumentalize oneself and to be able to participate, a fascist character is produced, spreading through the crowds (Sancho, 2018), enacting processes of exclusion and extermination. ...
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Based on data by researchers at the University of Warwick (UK), which indicate a positive relationship between Facebook use and the increase in attacks against refugees in Germany, the aim of this article is to establish a reference for understanding the functioning of social networks and their mediation role in the production and intensification of violence relations. This work resumes elements of the works of Theodor Adorno, Max Horkheimer, and Herbert Marcuse guided by the hypothesis of the existence of an ongoing forgetfulness in the social mediation organization. Instead of defending what would be a fair humanity, principles that keep barbarism as an active order are updated.
... This subsequently contributes to the digital footprint that people can track and see. Digital footprint is the data that internet users leave behind when they provide their details in any online activities (Thatcher, 2014;Lambiotte and Kosinski, 2014 ...
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... Verkompliziert wird das Einhalten von Normen des angemessenen Informationsflusses durch sich weiterentwickelnde Methoden der Datenverarbeitung. Künstliche Intelligenz ermöglicht nicht nur das automatisierte Treffen algorithmengestützter Entscheidungen, welche zur Steuerung und Organisation sozialer Systeme verwendet werden (Krafft & Zweig, 2019), sondern desgleichen die Extraktion "emergenter", privater Informationen aus "unverdächtigen" Datensätzen (Matz et al., 2019, Lambiotte & Kosinski, 2014Kosinski et al., 2013). In diesem Zusammenhang werden etwa aus Surfgewohnheiten einzelner Individuen auf probabilistische Weise private Informationen gewonnen, welche in der Folge etwa zu Zwecken der Anpassung von personalisierter Werbung oder Newsfeeds eingesetzt werden. ...
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Zusammenfassung Der Beitrag diskutiert aus ethisch-philosophischer Perspektive die Bedeutung des Konzepts Privatheit für individuelle Freiheit, Selbstverwirklichung und demokratische Teilhabe. Es wird dargestellt, dass in einer datafizierten Gesellschaft das private, individuelle Handeln durch umfassende Datenerhebungen abgebildet und für das politische Handeln fruchtbar gemacht werden kann . Auf diesem Wege können individuelle und private Handlungen durch ihre technische Verdichtung und Auswertung zu überindividuellen und öffentlichen Strukturbedingungen werden. Der Beitrag verdeutlicht, dass – trotz der Schutzwürdigkeit einer privaten Sphäre – private, individuelle Handlungen nicht zum bestimmenden Maßstab politischen Handelns werden dürfen. Dabei werden Argumente aus Demokratietheorie, Technokratiedebatte sowie Probleme der Widersprüche von Wertüberzeugungen und individueller Handlungspraxis ( value-action gap ) diskutiert. Vor diesem Hintergrund wird der Forschungsstand moderner Privatheitstheorien anhand einer Unterscheidung zwischen ihren individuellen und überindividuellen Dimensionen systematisiert. Die Beispiele Clouddienste und Medienmündigkeit vertiefen und veranschaulichen die Bedeutung von Privatheit und informationeller Selbstbestimmung für den Schutz der Demokratie.
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Araştırmada ortaokul öğrencilerinin “Dijital ayak izi” kavramına yönelik metaforik algılarını belirlemek amaçlanmıştır. Çalışma grubunu Sinop merkez ilçede 2021-2022 eğitim öğretim yılında öğrenim gören 144 öğrenci oluşturmaktadır. Veri toplama aracı olarak “Mülteci… ya/ye benzer; çünkü…” biçiminde yarım bırakılmış bir cümlenin tamamlanmasının istendiği soru formu kullanılmıştır. Araştırma nitel araştırma yöntemlerinden fenomenoloji deseni kullanılarak gerçekleştirilmiştir. Verilerin analizinde içerik analiz tekniği kullanılmıştır. Araştırma bulgularına göre öğrenciler “Dijital ayak izi” kavramını ifade etmek için 58 metafor üretmişlerdir. Öğrenciler “Dijital ayak izi” kavramını en çok GPRS, robot ayağı, parmak izi, uygulama ve beyin metaforlarına benzetmişlerdir. Öğrencilerin oluşturduğu metaforların 6 kavramsal kategoride toplandığı görülmektedir. Öğrenciler tarafından oluşturulan metaforlar Dijital Kişisel Veri İzleri, Dijital Nesne, Dijital Kalıcı Kayıt Veri Deposu, Dijital Bilgilerin Tarihselleşmesi, Dijital Takip, Dijital Kimlik Tanınma Aracı kategorileri altında toplanmıştır. Araştırma sonucunda dijital ayak izinin öğrenciler tarafından çoğunlukla kişiye özgü veriler olarak algılanmasının kavramla ilişkilendirildiğinde olumlu olduğu düşünülmektedir. Fakat metaforların açıklamaları incelendiğinde genel olarak “Dijital ayak izi” kavramının ortaokul öğrencileri tarafından tam olarak bilinmediği araştırma sonuçlarına yansımıştır. Anahtar Kelimeler: Metafor, Dijital Ayak İzi, Teknoloji, İnternet
Resumo Partindo dos dados elaborados por pesquisadores da Universidade de Warwick (UK), que indicam haver uma relação positiva entre o uso do Facebook e o aumento dos casos de ataques contra refugiados na Alemanha, o artigo tem como objetivo estabelecer um referencial para a compreensão da lógica de funcionamento das redes sociais e o seu papel de mediação na produção e intensificação das relações de violência. Para tal, o trabalho retoma elementos da obra de Theodor Adorno, Max Horkheimer e Herbert Marcuse em uma investigação que se orienta pela hipótese da existência da ação de um esquecimento em curso no modo como se organizam as mediações sociais. No lugar da defesa do que seria uma humanidade justa, são eleitos como fim princípios que se atualizam para manter a barbárie como ordem atuante.
Zusammenfassung Dieses Kapitel fokussiert den digitalen Fußabdruck einer Person, der typischerweise größer wird und der immer elaborierteren Auswertungsmöglichkeiten zugeführt wird. Die Autor:innen begründen die Vergrößerung des Fußabdrucks mit drei miteinander verschränkten technischen Trends: Hyperkonvergenz der Informationstechnologie, Hyperkonnektivität sowie mehr und mehr entstehende dynamische Informations-Ökosysteme. Um gezielt Gestaltungsvorschläge für privatheitsfreundliche Systeme entwickeln zu können, so die weitere Argumentation, müssen zunächst Angriffs- und Bedrohungspotenziale, die aus diesen Trends entstehen, betrachtet werden. Diese werden an Hand von vier Beispielen erläutert: Datenschutz im Domain Name System, bei mobilen Diensten wie Dating-Apps und Lern-Apps, bei vernetzten und smarten Objekten wie Smart-TVs oder Smart Cars sowie in öffentlichen, freien WLANs. Die Autor:innen zeigen mittels Generalisierung der gegebenen Beispiele das mögliche Spannungsverhältnis zwischen dem Innovationspotential von Digitalisierung und den wirtschaftlichen Interessen von Unternehmen auf der einen Seite sowie den Privatheitsinteressen der Nutzenden auf der anderen Seite auf. Dieses Spannungsverhältnis spiegelt auch der bestehende rechtliche Rahmen wieder. Die Autor:innen zeigen aber auch, dass Innovation und Privatheit durchaus vereinbar sind, beispielsweise wenn gerade mit und durch technische Innovationen geeignete „Privacy-enhancing Technologies“ (PETs) entwickelt und genutzt werden. Wie dies aussehen kann, wird an Hand von drei konkreten technischen Umsetzungsbeispielen (Me&MyFriends; WallGuard und Metaminer) illustriert. Me&MyFriends ist ein Selbstbewertungstool für Nutzer:innen, welches es ermöglicht, auf Basis von transparent gemachten Beziehungsgraphen detailliertes Wissen über hinterlassene digitale Spuren bei der Nutzung von Online Social Media zu erlangen. WallGuard dient der präventiven Erkennung von Social-Media-Beiträgen, die möglicherweise ein späteres Bereuen nach sich ziehen könnten. MetaMiner ist ein nutzerzentriertes Framework, das eine Verbesserung der Transparenz über die Netzwerkinteraktionen des mobilen Geräts ermöglicht. Gemeinsam ist den drei Gestaltungsvorschlägen, dass sie aufzeigen, wie wirkungsvoll Transparenz über den persönlichen digitalen Fußabdruck für den Schutz der informationellen Selbstbestimmung sein kann.
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Recent times exposed an increasing cybervetting exercise upon social networking in order to display career seekers. Previous research pointed out that many employing supervisors are not including applicants through good employing choices once the social media marketing content material is recognized as improper plus exhibit not professional personality. Using the decreasing associated with employability because of current financial problems , individual personalisation is observed vital to emphasize upon candidate's identification plus status to be able to long term companies. Even though previous correctly founded the idea of personalisation within placement customer items to become differentiated in addition to desired, study about individual logos continues to be hard to find, particularly in the framework associated with recruiting. This particular research is the conceptual evaluation about how private personalisation will help work applicants prove expertly. This particular studies depending on books evaluations in cybervetting, electronic impact as well as the part of private logos. Results demonstrated restricted correctly already been carried out inside Malaysia, around the function of private marketing within the old fashioned hiring procedure and framing profession character.
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Who can wait for larger, delayed rewards rather than smaller, immediate ones? Delay discounting (DD) measures the rate at which subjective value of an outcome decreases as the length of time to obtaining it increases. Previous work has shown that greater DD predicts negative academic, social, and health outcomes. Yet, little is known about who is likely to engage in greater or less DD. Taking a personality perspective, in a large sample (N = 5,888), we found that greater DD was predicted by low openness and conscientiousness and higher extraversion and neuroticism. Smaller amounts were also discounted more than larger amounts; furthermore, amount magnified the effects of openness and neuroticism on DD. Our findings show that personality is one predictor of individual differences in DD-an important implication for intervention approaches targeted at DD.
Conference Paper
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The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users’ personality with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem. Given the fast growing number of mobile phone subscription and availability of phone logs to researchers, our new personality indicators open the door to exciting avenues for future research in social sciences. They potentially enable cost-effective, questionnaire-free investigation of personality-related questions at a scale never seen before.
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Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.
This personal historical article traces the development of the Big-Five factor structure, whose growing acceptance by personality researchers has profoundly influenced the scientific study of individual differences. The roots of this taxonomy lie in the lexical hypothesis and the insights of Sir Francis Galton, the prescience of L. L. Thurstone, the legacy of Raymond B. Cattell, and the seminal analyses of Tupes and Christal. Paradoxically, the present popularity of this model owes much to its many critics, each of whom tried to replace it, but failed. In reaction, there have been a number of attempts to assimilate other models into the five-factor structure. Lately, some practical implications of the emerging consensus can be seen in such contexts as personnel selection and classification.
Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits.We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
Who can wait for larger, delayed rewards rather than smaller, immediate ones? Delay discounting (DD) measures the rate at which subjective value of an outcome decreases as the length of time to obtaining it increases. Previous work has shown that greater DD predicts negative academic, social, and health outcomes. Yet, little is known about who is likely to engage in greater or less DD. Taking a personality perspective, in a large sample (N = 5,888), we found that greater DD was predicted by low openness and conscientiousness and higher extraversion and neuroticism. Smaller amounts were also discounted more than larger amounts; furthermore, amount magnified the effects of openness and neuroticism on DD. Our findings show that personality is one predictor of individual differences in DD-an important implication for intervention approaches targeted at DD.
When a small consulting company in Chicago was looking to hire a summer intern this month, the company's president went online to check on a promising candidate who had just graduated from the University of Illinois. At Facebook, a popular social networking site, the executive found the candidate's Web page with this description of his interests: "smokin' blunts" (cigars hollowed out and stuffed with marijuana), shooting people and obsessive sex, all described in vivid slang. It did not matter that the student was clearly posturing. He was done. "A lot of it makes me think, what kind of judgment does this person have?" said the company's president, Brad Karsh. "Why are you allowing this to be viewed publicly, effectively, or semipublicly?" Many companies that recruit on college campuses have been using search engines like Google and Yahoo to conduct background checks on seniors looking for their first job. But now, college career counselors and other experts say, some recruiters are looking up applicants on social networking sites like Facebook, MySpace, Xanga and Friendster, where college students often post risqué or teasing photographs and provocative comments about drinking, recreational drug use and sexual exploits in what some mistakenly believe is relative privacy.
There is overwhelming anecdotal and empirical evidence for individual differences in musical preferences. However, little is known about what drives those preferences. Are people drawn to particular musical genres (e.g., rap, jazz) or to certain musical properties (e.g., lively, loud)? Recent findings suggest that musical preferences can be conceptualized in terms of five orthogonal dimensions: Mellow, Unpretentious, Sophisticated, Intense, and Contemporary (conveniently, MUSIC). The aim of the present research is to replicate and extend that work by empirically examining the hypothesis that musical preferences are based on preferences for particular musical properties and psychological attributes as opposed to musical genres. Findings from Study 1 replicated the five-factor MUSIC structure using musical excerpts from a variety of genres and subgenres and revealed musical attributes that differentiate each factor. Results from Studies 2 and 3 show that the MUSIC structure is recoverable using musical pieces from only the jazz and rock genres, respectively. Taken together, the current work provides strong evidence that preferences for music are determined by specific musical attributes and that the MUSIC model is a robust framework for conceptualizing and measuring such preferences.